Triple
T485573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Cantona |
E9867
|
entity |
| Predicate | disciplinaryRecord |
P13534
|
FINISHED |
| Object | banned from football for several months in 1995 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: banned from football for several months in 1995 | Statement: [Eric Cantona, disciplinaryRecord, banned from football for several months in 1995]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disciplinaryRecord Context triple: [Eric Cantona, disciplinaryRecord, banned from football for several months in 1995]
-
A.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
B.
academicProfile
Indicates the relationship that captures an entity’s academic background, qualifications, and scholarly activities or achievements.
-
C.
associatedWithDiscipline
Indicates that an entity has a relevant connection or involvement with a particular academic, professional, or thematic discipline.
-
D.
researchContribution
Indicates that an entity has produced or participated in creating new knowledge, findings, or innovations within a research context.
-
E.
academicReputation
Indicates the perceived quality and standing of an entity within the academic community, based on factors like scholarly impact, prestige, and recognition.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0bb46788190b40182bf2a54f98f |
completed | Feb. 28, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69a2edf48ec08190b85d07e194f99c49 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeba8a488190986cc7381332f783 |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.